Valuing Real Options in the Volatile Real World

• 成果类型: Article
• 署名作者: Harikae, Seiji; Dyer, James S.; Wang, Tianyang
• 署名单位: University of Texas System; University of Texas Austin; Colorado State University System; Colorado State University Fort Collins
• 刊物名称: PRODUCTION AND OPERATIONS MANAGEMENT
• ISSN/ISSBN: 1059-1478
• DOI: 10.1111/poms.13261
• 发表日期: 2021
• 页码: 171-189
• 关键词: extreme downside risk; multifactor real options; implied binomial trees

摘要

Motivated by the real-world challenges of real options evaluation faced by many companies when commodity prices exhibit dramatic volatility and project values can become negative, this study presents a framework for solving a multifactor real options problem by approximating the underlying stochastic process of project value with a generalized implied binomial tree. The proposed approach allows a flexible structure for stochastic processes with fat tail distributions, such as jump diffusion, regime switch or mean reversion and provides a more accurate estimate of the extreme downside risk by allowing negative values for the underlying project values. Our illustrative example shows that the value of a real option estimated by the proposed approach is more accurate and stable than the alternative lattice-based approaches in the literature under a wide variety of underlying commodity process, which makes this a more robust approach for valuing complex real options under multiple sources of uncertainty in the volatile real world.